License plate matching has become an increasingly popular method of regulating the ingress and egress of parking garages, toll roads, and private property. In various license plate matching systems, a video camera located near an entry point of the parking garage, toll road, or private property enables an operator to obtain and review license plate images of passing vehicles. The license plate images may be used to ascertain information regarding the vehicle, the driver of the vehicle, or the owner of the vehicle. Such imaging techniques also may also be found in numerous roadside enforcement systems, such as traffic light infraction systems.
Few reliable approaches exist for automatically reviewing license plate images and accurately recognizing characters and characteristics within those images. One approach includes Optical Character Recognition (OCR) during which alphanumeric characters from the image of the license plate are extracted based on their similarity to an abstract vector-like representation of a known letter or number. While frequently used, OCR processes have unpredictable accuracy and suffer significant performance inefficiencies when the obtained image is of low quality.
Accordingly, many OCR processes are combined with extensive image processing algorithms to force a higher quality image input or to compensate for errors in the extracted characters. Even with such corrective processes, often OCR-based systems require human review to ensure that extracted characters are being recognized properly. For example, in a variety of systems, such as traffic enforcement systems, automotive tolling systems, passive monitoring and data collection systems, and security monitoring systems, images that fall below a “confidence threshold” of the OCR process are designated for human review. Often this can include more than half of the images received.